Top DevOps Jobs in Dublin

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps practices, automation, CI/CD workflows, and monitoring frameworks; address performance issues, data drift, and operational incidents. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance groups to define success metrics, meet regulatory and ethical standards, and deliver business value. Lead cross-functional initiatives and influence outcomes without direct authority.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establish MLOps automation and workflows, manage model performance and data drift, respond to incidents, and conduct root cause analysis. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI solutions deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, manage incidents and root-cause analysis, and ensure model reliability, scalability, governance, compliance, and ethical operation. Partner with AI engineers, product teams, business stakeholders, and risk functions to define success metrics, translate business needs into technical requirements, and maximize the business value of AI solutions.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps practices, automation, reliability monitoring, and incident response processes. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical standards, resolve operational issues, and demonstrate business value.

Leads end-to-end deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establishes MLOps automation and workflows, manages model performance and data drift, responds to incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI deployments deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and workflows, resolve performance bottlenecks and data drift, manage operational incidents, and conduct root cause analysis. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet business objectives, and ensure deployments satisfy regulatory, security, and ethical AI requirements.

Leads deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps practices, automation, reliability frameworks, and incident response processes. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, resolve performance or data-drift issues, and ensure AI solutions deliver business value.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establishes MLOps automation and workflows, defines success metrics with stakeholders, manages model performance and data drift, ensures governance and compliance, and leads incident response and root-cause analysis. The role bridges technical and business teams while driving cross-functional initiatives and operational excellence.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps automation and CI/CD workflows, monitors performance and data drift, responds to incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI solutions deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, identify performance issues and data drift, manage incidents and root-cause analysis, and ensure reliable, scalable operations. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and connect AI deployments to business objectives.

Lead end-to-end deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and workflows, identify performance issues and data drift, lead incident response and root-cause analysis, and ensure reliable, scalable deployments. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to translate business needs into technical requirements, measure outcomes, and meet regulatory and ethical AI standards.

Lead and develop globally distributed Data and AI teams to deliver production Data/AI solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to set priorities, roadmaps, and delivery metrics.

Leads the design and evolution of an enterprise AI platform supporting experimentation through production. Manages and mentors AI engineers, defines the technical roadmap, and develops shared capabilities such as model serving, feature stores, vector databases, AI gateways, and registries. Establishes MLOps and LLMOps standards, cloud-native infrastructure, governance, security, observability, reliability, and automation practices. Partners with architecture, cloud, data, security, product, and data science teams to scale secure, resilient, and cost-effective AI adoption.

Lead the design and evolution of an enterprise AI platform supporting experimentation through production. Manage and mentor AI engineers, define platform roadmaps, establish MLOps and LLMOps standards, and build services for model serving, inference, feature stores, vector databases, governance, and observability. Design secure cloud-native infrastructure using Kubernetes and infrastructure as code, ensure reliability and scalability, productionize AI solutions, and collaborate with architecture, security, data, product, and science teams.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, feature stores, governance, observability, and self-service capabilities. Manage and mentor AI engineers while defining platform strategy and technical roadmaps. Establish MLOps and LLMOps standards, build cloud-native infrastructure, and ensure scalability, reliability, security, compliance, and cost efficiency. Partner with data scientists, security, architecture, and product teams to productionize AI and generative AI solutions across the enterprise.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, MLOps, LLMOps, governance, observability, and cloud-native infrastructure. Manage and mentor AI engineers, define the technical roadmap, productionize AI and generative AI solutions, and ensure platform security, scalability, reliability, compliance, and cost efficiency across the enterprise.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, feature stores, vector databases, governance, observability, and developer self-service. Manage and mentor AI engineers, establish MLOps and LLMOps standards, drive cloud-native infrastructure and reliability, and partner with architecture, security, data, product, and data science teams to productionize scalable, secure AI and Generative AI solutions.

Leads the design and operation of Mastercard’s enterprise AI platform, including model serving, MLOps, LLMOps, cloud-native infrastructure, governance, security, observability, and reliability. Manages and mentors AI engineers, defines the platform roadmap, partners with architecture, security, data, and product teams, and enables production deployment of AI and generative AI solutions at enterprise scale.

Lead the design and operation of an enterprise AI platform supporting production AI and Generative AI solutions. Manage and mentor AI engineers, define platform roadmaps, build shared services for model serving and orchestration, establish MLOps and LLMOps standards, and ensure scalability, reliability, security, governance, and compliance. Partner with architecture, cloud, data, security, product, and data science teams to accelerate enterprise AI adoption.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, registries, and developer tools. Manage and mentor AI engineers, define the platform roadmap, establish MLOps and LLMOps standards, and ensure secure, scalable, reliable cloud-native infrastructure. Partner with architecture, security, data, product, and science teams to productionize AI solutions while implementing governance, observability, compliance, automation, and operational excellence.

Lead the design and evolution of an enterprise AI platform supporting experimentation through production. Manage and mentor AI engineers, establish MLOps and LLMOps standards, build cloud-native services for model serving and orchestration, and ensure scalability, reliability, security, governance, and cost efficiency. Partner with architecture, cloud, data, security, product, and data science teams to productionize AI and generative AI solutions while driving platform strategy, operational excellence, and engineering best practices.

Senior Site Reliability Engineer responsible for ensuring reliability, scalability, and performance of Mastercard applications. Drive observability, automation, capacity planning, incident triage, root-cause analysis, and proactive improvements. Collaborate with developers to embed operational design, implement CI/CD and infrastructure-as-code, and apply ITSM practices to manage incidents, changes, and risk while mentoring peers and contributing to operational standards and documentation.

Lead technology and cyber risk compliance for global banking technology. Advise on regulations (DORA, EU AI Act, NIS2), perform impact/gap assessments, review incidents, guide cloud and AI adoption, apply data analytics, and partner with stakeholders to embed controls and ensure operational resilience.

Lead design, build, automate, and operate enterprise PostgreSQL and Cassandra platforms. Ensure high availability, performance tuning, backup/recovery, DR, monitoring, and cloud adoption. Provide technical leadership, collaborate with infrastructure and security teams, and drive platform modernization and automation via IaC and CI/CD.

Lead design and architecture of cloud-native, large-scale distributed systems; drive engineering excellence, PDLC discipline, AI/automation in engineering, mentor engineers, and partner across teams to deliver secure, reliable platforms.